Instructions to use devansvd/bert-model-test-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devansvd/bert-model-test-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devansvd/bert-model-test-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devansvd/bert-model-test-2") model = AutoModelForTokenClassification.from_pretrained("devansvd/bert-model-test-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results_ner.txt from devansvd/bert-model-test-2: direct link, hf CLI and curl.
- Browser
- Download file 136 Bytes
-
https://huggingface.co/devansvd/bert-model-test-2/resolve/main/eval_results_ner.txt
- Command line
-
hf download hf://devansvd/bert-model-test-2/eval_results_ner.txt
-
curl -L -o eval_results_ner.txt https://huggingface.co/devansvd/bert-model-test-2/resolve/main/eval_results_ner.txt
136 Bytes
- Xet hash:
- 8c609a61a3ce95116b772e521d62f032e40eccfcb42d897ef9def9cf37bfd4ca
- Size of remote file:
- 136 Bytes
- SHA256:
- 352ae954be3d099cd16f20a0ba077a013e09f9683c7c834d51293b8018526bbc
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